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Engineering >> 2022, Volume 8, Issue 1 doi: 10.1016/j.eng.2021.12.002

Federated Learning for 6G: Applications, Challenges, and Opportunities

a Department of Electronic and Electrical Engineering, University College London, London WC1E 6BT, UK
b Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
c Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong, Shenzhen 518172, China
d School of Science and Engineering and Future Network of Intelligence Institute, the Chinese University of Hong Kong, Shenzhen 518172, China 

Received:2021-01-01 Revised:2021-06-13 Accepted: 2021-10-15 Available online:2021-12-08

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Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.


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